Does Climate Play Any Role in COVID-19 Spreading?—An Australian Perspective

Abstract

Compared to other countries, the COVID-19 pandemic did not severely affect Australia as measured by total deaths until mid-2021. Though a substantial number of daily confirmed cases (up to 698) were reported during the second wave, most of them were from the southern state of Victoria. This study examined the possible correlations between climate variables and the number of daily confirmed COVID-19 cases in Victoria, Australia, from 25 January to 31 October 2020. Appropriate regression models and cross-correlation diagnostics were used to examine the effect of temperature, rainfall, solar exposure, and ultraviolet index (UVI) with the number of daily confirmed cases. Significant positive associations were identified for solar exposure and maximum and average UVI for confirmed cases one and 19 days later. Negative associations for these variables were found for confirmed cases five days later. Minimum temperature had a significant negative correlation one day later and a positive effect 21 days later. No significant correlation was found for maximum temperature and rainfall. The most significant relationships were found for confirmed cases 19 days after changes in the meteorological variables. A 1% increase in solar exposure, maximum UVI, and average UVI was associated with a 0.31% (95% CI: 0.13 to 0.51), 0.71% (95% CI: 0.43 to 0.98), and 0.63% (95%CI: 0.20 to 1.61) increase 19 days later in the number of confirmed cases, respectively. The implications of these results can be used in the public health management of any possible future events in Australia. It also highlights the significance of considering the climatic variables and seasonality in all kinds of epidemics and pandemics.

Correlation between COVID-19 and weather variables: A meta-analysis

“…High humidity, low wind speed, and relatively lower air temperature. Abraham J et al [ 64 ] 2021 Victoria, Australia 25 January to 31 October 2020 281 Linear regression, Pearson, Spearman correlation 8 Temperature, humidity, and rainfall Minimum temperature had a significant negative correlation and a positive effect 21 days later. No significant correlation was found between maximum temperature and rainfall.…”

Using weather factors and google data to predict COVID-19 transmission in Melbourne, Australia: A time-series predictive model

“…However, there has been limited research on short-term COVID-19 forecasting in an Australian context [ [62] , [63] , [64] ], and developing and evaluating predictive modelling using environmental variables with internet-based data for increased accuracy in this geospatial context [ 65 ]. Most time-series forecasting studies to date primarily deal with early outbreaks in the pandemic, mortality rates or northern hemisphere transmission patterns [ [66] , [67] , [68] ].…”

Assessment of 24-h moving average PM2.5 concentrations in Bangkok, Thailand against WHO guidelines

“…Similarities in terms of fluctuation shape and distance between the two time series were analyzed using CCF and Euclidean distance, respectively. Several studies have used CCF for various reasons-for example, to examine the association between confirmed cases of COVID-19 and meteorologic variation [26] to evaluate the relationship between the El Nino-Southern Oscillation variability represented by the Southern Oscillation Index and the associated time series of the number of new fish [27], and to investigate the lead-lag relationship between the two time series at different time points; CCF can also be used to determine the optimal time shift between the two time series [27,28]. The correlation coefficients of 1 and − 1 indicate perfect relationships in the same and opposite directions, respectively, with other positive or negative values implying the following: no relationship (0), almost negligible relationship (< 0.2), small relationship (0.2-0.4), substantial relationship (0.4-0.7), marked relationship (0.7-0.9) and very dependable relationship (0.9-1.0), respectively [29].…”